Metadata-Version: 2.1
Name: sequentia
Version: 0.1.0
Summary: A machine learning interface for isolated temporal sequence classification algorithms in Python.
Home-page: https://github.com/eonu/sequentia
Author: Edwin Onuonga
Author-email: ed@eonu.net
License: MIT
Project-URL: Bug Tracker, https://github.com/eonu/sequentia/issues
Project-URL: Source Code, https://github.com/eonu/sequentia
Description: # Sequentia
        
        _A machine learning interface for isolated temporal sequence classification algorithms in Python._
        
        ## Introduction
        
        <img src="https://i.ibb.co/gPymgs4/classifier.png" width="40%" align="right"></img>
        
        Temporal sequences are sequences of observations that occur over time. Changing patterns over time naturally provide many interesting opportunities and challenges for machine learning.
        
        This library specifically aims to tackle classification problems for isolated temporal sequences by creating an interface to a number of classification algorithms.
        
        Despite these types of sequences sounding very specific, you probably observe some of them on a regular basis!
        
        **Some examples of classification problems for isolated temporal sequences include classifying**:
        
        - isolated word utterances in speech audio signals,
        - [isolated hand-written characters according to their pen-tip trajectories](./examples/Pen-Tip%20Trajectories%20(Example).ipynb),
        - isolated hand or head gestures in a video or motion-capture recording.
        
        ## Features
        
        Sequentia offers the use of **multivariate observation sequences with differing durations** in conjunction with the following algorithms and methods.
        
        ### Classication algorithms
        
        - [x] Ensemble Hidden Markov Models
          - [x] Multivariate Gaussian emission distributions
          - [ ] Gaussian Mixture Model emission distributions (soon!)
        - [x] Approximate Dynamic Time Warping k-Nearest Neighbors (implemented with [FastDTW](https://github.com/slaypni/fastdtw) – also see [[1]](#references))
        - [ ] Long Short-Term Memory Networks (soon!)
        
        ### Preprocessing methods
        
        - [x] Sequence Normalization
        - [x] Downsampling (by decimation and averaging)
        - [x] Discrete (Fast) Fourier Transform
        
        > **Disclaimer**: The package currently remains largely untested and is still in its first alpha stages – use carefully!
        
        ## Installation
        
        ```
        pip install sequentia
        ```
        
        ## References
        
        <table><tbody><tr><td>[1]</td><td><a href="https://pdfs.semanticscholar.org/05a2/0cde15e172fc82f32774dd0cf4fe5827cad2.pdf">Stan Salvador, and Philip Chan. <b>"FastDTW: Toward accurate dynamic time  warping in linear time and space."</b> Intelligent Data Analysis 11.5  (2007): 561-580.</a></td></tr></tbody></table>
        
        # Contributors
        
        All contributions to this repository are greatly appreciated. Contribution guidelines can be found [here](/CONTRIBUTING.md).
        
        <table>
        	<thead>
        		<tr>
        			<th align="center">
                        <a href="https://github.com/eonu">
                        <img src="https://avatars0.githubusercontent.com/u/24795571?s=460&v=4" alt="Edwin Onuonga" width="60px">
                        <br/><sub><b>Edwin Onuonga</b></sub>
                        </a>
                        <br/>
                        <a href="mailto:ed@eonu.net">✉️</a>
                        <a href="https://eonu.net">🌍</a>
        			</th>
        			<!-- Add more <th></th> blocks for more contributors -->
        		</tr>
        	</thead>
        </table>
        
        ---
        
        <p align="center">
          <b>Sequentia</b> &copy; 2019-2020, Edwin Onuonga - Released under the <a href="https://opensource.org/licenses/MIT">MIT</a> License.<br/>
          <em>Authored and maintained by Edwin Onuonga.</em>
        </p>
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Programming Language :: Python :: 3.5
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Natural Language :: English
Requires-Python: >=3.5
Description-Content-Type: text/markdown
